System and Method of Workforce Optimization

ABSTRACT

A method of workforce optimization includes acquiring video data. The video data is obtained from a plurality of video cameras in a facility comprising a plurality of departments. A customer load for each of the plurality of departments is identified. A location of each of a plurality of employees in the facility is identified. A customer-to-employee ratio is determined for each department. The determined customer-to-employee ratio for each department is provided to a computing device. At least one employee deployment notification is provided from the computing device to another computing device.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.14/263,403 filed on Apr. 28, 2014, which claims the benefit of U.S.Provisional Patent Application No. 61/839,508, filed on Jun. 26, 2013.The contents of each of these applications is hereby incorporated hereinby reference in its entirety.

BACKGROUND

In a retail store setting, customer interactions with customer serviceemployees can improve the customer experience and facilitate increasedsales. Current methods of customer service employee deployment within aretail facility is generally based upon management experience andgeneralized impressions of customer flow and customer-employeeinteractions within various sales departments. Therefore, thesedeterminations are highly subjective which can result in greatinefficiencies in the deployment and management of customer serviceemployees across sales departments in a retail facility.

BRIEF DISCLOSURE

The present disclosure relates to systems and methods for identificationof customers and employees within a retail facility and data drivensolutions for optimization of the deployment of customer serviceemployees within the retail facility.

An exemplary embodiment of a method of workforce optimization includingacquiring video data obtained from a plurality of video cameras in afacility comprising a plurality of departments. A customer load in eachof the plurality of departments is identified. A location of each of aplurality of employees in the facility is identified. Acustomer-to-employee ratio is determined for each department from theidentified customer load and the identified location of each of theplurality of employees. The determined customer-to-employee ratio foreach department is provided to a computing device associated with amanager. at least one employee deployment notification is provided fromthe computing device associated with the manager to a computing deviceassociated with at least one employee.

In additional exemplary embodiment of a method of workforce optimizationincludes acquired, video data obtained by a plurality of camerascovering a plurality of departments in a facility. A customer load ineach of the plurality of departments is identified with the computerprocessor from the video data. A location within the facility of each ofa plurality of employees is identified with the computer processor formthe video data. Employee deployment analytics are determined for eachdepartment with a computer processor from the customer load for eachdepartment and the location of each of the plurality of employees. Atleast one suggested employee deployment is determined with the computerprocessor from the employee deployment analytics and the location ofeach of the plurality of employees. The at least one suggested employeedeployment is provided from the computer processor to a remotely locatedcomputing device.

An exemplary embodiment of a non-transient computer readable mediumprogrammed with computer readable code that upon execution by a computerprocessor causes the processor to optimize a workforce. The processoracquires video data obtained from a plurality of video cameras in afacility comprising a plurality of departments. The processor identifiesa customer load in each of the plurality of departments from the videodata. The processor further identifies a location within the facility ofeach of a plurality of employees from the video data. Employee data foreach of the plurality of employees is acquired by the processor.Department data for each of the plurality of departments is acquired bythe processor. The processor determines a customer-to-employee ratio foreach department from the customer load for each department and alocation of each of the plurality of employees. A plurality of suggestedemployee deployments is determined by the processor from the employeedata, the department data, the customer-to-employee ratio, and thelocation of each of the employees. The computer processor provides theplurality of suggested employee deployments to a remote computingdevice.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a system diagram of an exemplary embodiment of a system forworkforce optimization.

FIG. 2 is a flow chart that depicts an exemplary embodiment of a methodof workforce optimization.

FIG. 3 depicts an exemplary screen shot from a computing deviceassociated with a manager.

FIG. 4 depicts an alternative embodiment of an exemplary screen shotfrom a computing device associated with a manager.

FIG. 5 depicts an exemplary screen shot from a computing deviceassociated with a customer service employee.

FIG. 6 is a system diagram of an exemplary embodiment of a computingsystem for workforce optimization.

DETAILED DISCLOSURE

FIG. 1 depicts an exemplary embodiment of a workforce management system10 as disclosed herein. A workforce management system 10 is exemplarilyconfigured to manage the workforce of a facility, exemplarily a retailstore facility, with a plurality of departments 12, exemplarilyidentified as “Department A”, “Department B”, “Department C”, and“Department D.” In a merely exemplary embodiment, the departments 12 mayinclude departments such as clothing, home goods, shoes, or jewelry in adepartment store, while departments such as lawn and garden, paint,tools, and building materials may be found in a home improvement store.These examples are merely exemplary of the retail settings ordepartments therein in which embodiments may be implemented. Eachdepartment 12 is outfitted with one or more video cameras 14 thatoperate as disclosed herein to obtain video data of the department 12from which real time or near real time determinations of people countswithin the department may be made. The people counts may be furtherrefined as disclosed herein to distinguish employees, exemplarilycustomer service employees, from customers.

The video data is provided from the video cameras 14 to a workforcemanagement server 16 and exemplarily to a workforce allocator 18operating on the workforce management server 16. In an embodiment, theworkforce allocator 18 processes the video data to determine the peoplecounts from the video data, including the identification of customersand employees. In an alternative embodiment, a separate computer orcomputer program operating on a computer (not depicted) receives thevideo data, processes the video data to identify people within the videodata, and categorizes the identified people as either customers oremployees. In further embodiments, the video data can be analyzed totrack individual customers or individual employees in movements bothwithin a department and across departments. In still furtherembodiments, employees may each have an electronic device associatedwith the employee, which exemplarily may be an RFID tag, or a mobilecomputing device, exemplarily a smart phone. The electronic device istracked by an employee tracking system to provide further employeelocation data that is analyzed along with the video data in order toconfirm and refine the identification of employees within the video dataand the locations of employees in the department or facility. In suchembodiments, the verification of the locations of particular employeesin the video data may allow for the identification and/or tracking ofspecific employees within the video data. In alternative embodiments,specific employees may be identified and/or tracked in the video data byrecognition of employee physical features in the video data.

The workforce management server 16 includes a variety of sources of datathat is provided to, and used by, the workforce allocator 18 asdescribed in further detail herein. The workforce management server 16includes employee schedules 20, employee data 22, and department data24. The employee schedules 20 may include start, stop, and break timesfor individual employees, as well as a particular task to which theemployee is assigned during the scheduled times. Non-limiting examplesof assigned employee tasks, may include assignment of an employee to aparticular department, assignment to a floating position between two ormore departments, or assignment to a particular task, such as, but notlimited to, product restocking or product re-facings. However, these aremerely exemplary embodiments of jobs to which the employee may beassigned.

The employee data may include employee identification information, suchthat the employee data may be cross referenced and are associated withan employee schedule and/or an employee identification such as obtainedby tracking an RFID, cell phone, or other mobile computing deviceassociated with the employee as described above. The employee data 22may further include an identification of the departments in which theemployee has expertise, which may be identified as “primary”departments, and an identification of departments in which the employeehas received at least basic knowledge or training, which may beidentified as “secondary” departments.

The department data 24 may include the identification of each of thedepartments, an expertise used in that department, a predeterminedtarget customer-to-employee ratio for the department, a boundary of thedepartment, and a geographic proximity to the other departments withinthe facility.

The workforce allocator 18 receives the employee schedule 20, employeedata 22, department data 24, and the video data from the video cameras14 or the associated real time people counts obtained therefrom and usesthis information as described in further detail herein in order todetermine a departmental customer load for each of the departments 12.In an embodiment, the workforce allocator 18 further identifies acustomer-to-employee ratio from the determined departmental customerload and the identified employees in the department. Thecustomer-to-employee ratio is compared to the targetcustomer-to-employee ratio from the department data 24 in order todetermine if there is a need to reassign additional employees from otherdepartments or tasks to a department in need of additional customerservice employees.

The workforce allocator 18 is communicatively connected to a computingdevice 26 associated with a manager. It is to be recognized that inembodiments, the workforce allocator 18 may be communicatively connectedto a plurality of computing devices 26 that may be associated with aplurality of managers, but for objective of conciseness in thedescription of this exemplary embodiment, a single computing deviceassociated with a manager 26 will be described.

The workforce allocator 18 may also be communicatively connected to acomputing device 46 associated with an employee. Similar to thecomputing device 26 associated with the manager, for the sake ofsimplicity, only a single computing device 46 is depicted, but it isunderstood that in embodiments, a computing device 46 may be associatedwith each employee of a plurality of employees. In an embodiment, thecomputing device associated with the manger is a smart phone or otherhandheld computing device 26. FIGS. 3 and 4 depict exemplary embodimentsof screen shots of a first exemplary graphical user interface (GUI) 28and a second exemplary graphical user interface (GUI) 30 that may bepresented on a graphical display 27 of the the computing device 26associated with the manager.

Referring to FIG. 3, the first GUI 28 includes an employee deploymentqueue 32 which includes a plurality of automatedly suggested employeedeployments 34. Each suggested employee deployment 34 in the employeedeployment queue 32 includes a department identification 36, acustomer-to-employee ratio 38, and a suggested deployment 40. In anembodiment, the employee deployment queue 32 may list each of thesuggested employee deployments 34 in descending order from mostimportant to least important, while in an alternative embodiment, thesuggested employee deployments 34 may be color coded based upon need ordesirability of the suggested deployment 34. In a non-limiting examplethe color coding may include red for urgent suggestions, yellow fornon-urgent suggestions, and green for departments with no suggesteddeployments.

FIG. 4 depicts an alternative embodiment of the GUI, exemplary secondGUI 30. Second GUI 30 reports only a single suggested employeedeployment 34, exemplarily for a most critical or urgent employeedeployment. The rest of the space in the GUI 30 is devoted to a videoplayer which may present video data from the department with thesuggested deployment. Embodiments may stream real time video data of thedepartment that coincides with the suggested deployment. This enables amanager to view the real time status of the department in the video databefore initiating the suggested deployment 40.

In the second GUI embodiment 30, the manager may use a swiping motion orother navigational gesture to cycle through a plurality of suggestedemployee deployments 34, which include the associated real time videodata of the department.

In non-limiting embodiments of the GUI in FIGS. 3 and 4, the manager mayselect and initiate one or more of the suggested employee deployments 34by touching or otherwise selecting one or more of the suggested employeedeployments 34. Upon this selection, the computing device associatedwith the manager 26 creates and sends and employee notification signal44 to a computing device associated with the employee 46. In analternative embodiment, the employee notification signal 44 may berouted through the workforce allocator 18 of the workforce managementserver 16. Similar to the computing device 26 associated with themanager, the computing device 46 associated with the employee may be anyvariety of computing devices, including, but not limited to a smartphone or other hand held computing device. The employee notificationsignal 44 may be transmitted in a variety of manners, across a varietyof communication platforms, including, but not limited to Wi-FiBluetooth, IR, near-field communications, or other device to devicecommunication platforms. In alternative embodiments, the employeenotification signal 44 may be sent as SMS, e-mail, or internet protocol(IP) transmissions.

FIG. 5 depicts an exemplary embodiment of the graphical user interface38 that may be presented on a graphical display 47 of the computingdevice 46 associated with the employee. The GUI 48 presents an employeenotification 50 associated with the received employee notificationsignal 44. This can inform the employee to carry out the employeedeployment that was suggested by the system to the manager andapproved/selected by the manager as indicated by the manager's selectionof one or more of the suggested employee deployments presented at thecomputing device 26.

The GUI 48 provides a quick response list 52 that includes a pluralityof predetermined responses 54 from which the employee may select aresponse to be sent back as a response signal 56 to the computing device26 associated with the manager. It will be recognized that in anembodiment, the response signal 56 may be sent to the computing device26 associated with the manager in the same communication format as theemployee notification signal 44 is sent to the computing device 46associated with the employee.

Upon receiving the response at the computing device 26 associated withthe manager, the manager, exemplarily using a GUI as depicted in FIG. 4,may check in to the department to which the employee was deployed on thereal time video display such as to confirm that the employee has actedupon the instructions and to check in on the customer service status ofthe department. In embodiments, it will be recognized that the workforceallocator 18 will identify and acknowledge that an employee has movedbetween departments 12 and update the determined departmental loads andcustomer-to-employee ratios in each department. In still furtherembodiments, it is to be recognized that the workforce allocator 18 may,upon certain determined criteria, automatedly send employee notificationsignals directly to one or more computing devices 36 associated withemployees to enact automated employee deployments.

FIG. 2 is a flow chart that depicts an exemplary embodiment of a method100 of workforce optimization. The method 100 begins at 102 with theacquisition of video data from a plurality of video cameras distributedthroughout a facility, exemplarily a retail store. In an embodiment, thevideo data is acquired with respect to a plurality of departments orregions defined within the facility. The video data can be acquired andstreamed in real time for real time processing in a manner as disclosedherein, while in other embodiments, the video data may be recorded in adigital format for later processing and analysis, including in themanners as described herein. However, it will be understood that theembodiments as described herein will be exemplarily described in areal-time or near-real time implementation, while it will be recognizedthat similar techniques may be implemented in a time-shiftedimplementation.

The video data acquired at 102 is processed at 104 to identify adepartmental customer load and the video data is processed at 106 toidentify the locations of employees within the facility. In anembodiment, this video processing may first start with analysis of thevideo data to identify and count people within each department basedupon the video data associated with that department. In an embodiment,this raw person count may be used as the identified departmentalcustomer load 104. In a more refined embodiment, after the video datahas been processed to identify the people in the department, the personidentifications are then refined to identify the employees among all ofthe people in the department and therefore identify at least oneemployee location 106. In non-limiting embodiments, employees may wearclothing or another identifiable feature that may facilitate theidentification of employees within the video data. For example employeesmay wear a distinctive white hat or a particular color shirt.Identification of a feature such as this may indicate a high likelihoodthat an individual is an employee.

In an exemplary embodiment, auxiliary employee location data is receivedat 108 and this auxiliary employee location data is used in conjunctionwith the analyzed video data to refine and confirm the identifiedemployee locations, including in some embodiments, the identification ofspecific employees within the department or facility. In a non-limitingembodiment, the auxiliary employee location data may be received at 108from an RFID tag associated with the employee that is tracked ortriangulated between antennas of a wireless network deployed throughoutthe facility. In an alternative embodiment, the employees may each carrya mobile computing device, exemplarily a smart phone or other hand heldcomputer that includes GPS and a GPS location signal may be received at108 as the auxiliary employee location data.

Once the departmental customer load is identified at 104 and theemployee locations are identified at 106, a variety of workforceoptimization techniques and analytics may be carried out, as willdescribed in further detail herein.

In one embodiment, the identified employee location 106 may be furtheranalyzed to identify undesirable employee actions 110 and/or to mapemployee ranges at 112. Undesirable employee actions identified at 110may include identification of employee clustering which may be a sign ofinefficient customer service and workforce deployment within a facility.Employee clustering may be identified as two or more employees gatheredwithin a close proximity (e.g. maximum specified distance) to oneanother for a specified minimum time period. At 112, individualemployees may be tracked in the acquired video data over the course of atime period exemplarily an hour, a shift, a day, or across multipledays. A map of the facility is created that shows the identifiedlocations of the employee. This can be exemplarily depicted as a line orpath that the employee traveled over the specified time period, or aheat map that integrates employee location and the length of time atthat location. This can provide information as to an effective range inwhich an employee can provide customer service within a department.Alternatively, the mapped employee ranges may identify employees thatstray or move out of an assigned department or customer service area.Any identified undesirable employee action at 110 and mapped employeeranges at 112 may be provided for later analysis and workforceoptimization as disclosed in further detail herein.

At 114 employee data is acquired. The acquired employee data may includeemployee schedule information, which may identify employee start times,stop times, and breaks. The employee data may further include a job ortask to which the employee is assigned for that day or portion of theschedule. This assigned job or task may be an assignment to adepartment, to a task, or to an “overflow” position. In still furtherembodiments, the employee data may include a employee identificationsuch as an employee name, or identification member. The employee datamay further include identification of employee skills or expertise. Theidentified employee skills or expertise may include an identification ofone or more departments in which the employee is skilled, trained, orexperienced. Such departments may be identified as “trained” or“primary” departments. The employee data further identify otherdepartments, where the employee may also work but does not have as muchtraining or experience. These departments may be identified as“secondary” departments. Departments in which the employee has noexpertise, skill, training, or experience may be identified as“untrained” or “tertiary” departments. In a still further embodiment,each of the departments may be ranked for the employee based upon thatemployee's expertise, skill, training, or experience. Such ranking orother identification of employee skills may exemplarily result fromroutine performance reviews, a log of employee department assignments,or an online record of training courses or seminars in which theemployee has participated.

At 116 department data is acquired. The acquired department data mayinclude information such as department identification information whichmay identify the department, products sold therein, or the geographicboundaries of the department within the facility. The department datamay further include information regarding the proximity or location ofone department to other departments or within a facility. As describedin further detail herein, such relative location information canfacilitate determinations for optimal employee deployment. In stillfurther embodiments, each department may have a predetermined targetcustomer-to-employee ratio that may be used and modified as furtherdetailed herein. This department data can be specified on a facility byfacility basis, adjusted by a manager, or established on a company-widebasis. In still further embodiment, department data, exemplarily targetcustomer-employee ratio may be automatedly determined as describedherein and adjusted when new determinations are made.

Next, at 118 employee deployment analytics are determined in anembodiment. Such employee deployment analytics may be determined by theworkforce allocator 18 as depicted in FIG. 1. The employee deploymentanalytics may be determined based upon one or more of the departmentcustomer load identified at 104, employee locations identified at 106,employee data acquired at 114, department data acquired at 116,undesirable employee actions identified at 110, and employee rangesmapped at 112. The employee deployment analytics 118 can include suchdeterminations and aggregations based upon the received information.

In one example, based upon the identified departmental customer load andidentified employee location, at 120 a customer-to-employee ratio can beidentified. This identified customer-to-employee ratio can be comparedto the predetermined target customer-to-employee ratio from the acquireddepartment data to determine if more employees are required in adepartment or if that department is currently overstaffed and can lendan employee to a busier department.

If one department is determined to be above the pre-determined targetcustomer-to-employee ratio then at 122 a suggested employee deploymentis determined. The suggested employee deployment may be determined basedupon the customer-to-employee ratios determined for each of thedepartments in comparison to that department's predetermined targetcustomer-to-employee ratio, the proximity of one department to another,which may relate to the time required to enact the employee deployment,and the training or skills of specific employees in each of thedepartments are required for the department in need of help. Still otherconsiderations may be used, including business priorities in particulardepartments, sales volume of departments, sales margins of departments,or determined effectiveness of customer-employee interactions fordepartments. Based upon one or more of these considerations, at leastone suggested employee deployment is determined at 122. These, or other,considerations may also be used to determine a priority or a relativepriority for each of a plurality of suggested employee deployments.

The at least one suggested employee deployment is presented to a mangerat 124 exemplarily as described above with respect to FIG. 3 or FIG. 4.In such embodiments, the manager is presented with one or more suggestedemployee deployments on a mobile computing device associated with themanger. The manager can review the suggested employee deployments, andin some embodiments view the real time video data of the departmentbefore selecting/confirming that one or more of the suggested employeedeployments are to be carried out. Upon selection or confirmation by themanager, an employee notification is presented at 126 at a mobilecomputing device associated with the employee such that the employeeknows to move to another department to provide customer service. Asdescribed above with respect to FIGS. 3-5, the employee may also bepresented with one or more response selections, which may be sent by theemployee such that the manager receives a confirmation or clarificationas to when the employee will act upon the deployment notification.

The employee deployment analytics at 118 can further be used inalternative manners as well. Exemplarily, the acquired video data,identified departmental customer loads, and, in embodiments, theidentified employee locations can be used at 128 to identify customerservice hot spots which may be departments or portions of departmentsthat experience a high degree of customer traffic and/or need and inwhich customer service needs are not met by the employees deployed tothat department or area. In an exemplary embodiment, customer serviceneed may be evaluated based upon an identified customer dwell time at aparticular location without an interaction with a customer serviceemployee. At 130 a notification of such identified customer services hotspots can be provided. In exemplary embodiments, the notification can beprovided as a map, which may be a “heat map” showing customer serviceneed, such a notification can be presented on the mobile computingdevice associated with the manager, or may be used by management afterthe fact, apart from any real time analysis. Such time-shifted oroffline analysis may be used to make or inform larger managementdecisions regarding store layout or employee assignments.

In still other embodiments, the notification of a hot spot at 130 may bean indication of a particular department, location, or product display,if such data is available and can be cross referenced with theidentified customer locations.

The employee deployment analytics determined at 118 can further be usedat 132 in order to perform department optimization, which may includeone or more suggestions or automated changes to the store departmentdata previously acquired at 116. In a non-limiting embodiment, suchdepartment optimization performed at 132 may be to adjust a targetcustomer-to-employee ratio for a particular department such as toreflect the actual need of that department or actualcustomer-to-employee ratios experienced within a department. Inalternative embodiments, department optimization at 132 may furtherinclude adjustments to the employee schedule or other employee dataacquired at 114. Such department optimizations at 132 may be to adjustthe staffing or assignments of employees to particular departments, orto suggest initiating employee training such that more employees areskilled or competent to work in departments that experience highcustomer service needs.

In still further embodiments, the departmental customer load identifiedat 104 and the employee locations identified at 106, potentially inaddition to the video data acquired at 102 and the auxiliary employeelocation data received at 108 may be used at 134 to identify specificcustomer-employee interactions. The identification of such specificcustomer-employee interactions may require further video data processingbeyond the identification and counting of customers and identificationof employee locations, and may further seek to pick out actualcustomer-employee interactions. In an embodiment, the identifiedinteractions and the location at which these interactions occur can betranslated to the products in a general vicinity of thecustomer-employee intonation. By tracking the customer after thecustomer-employee interaction through the customer check out, customerpoint-of-sale data can be acquired at 136 and correlated with thespecific customer-employee interactions that the customer had while atthe retail facility. This combined information regarding the customerpurchases and the instances of customer-employee interactions can becompared at 138 to known customer conversion rates for various productsand/or data acquired from customers that do not experience acustomer-employee interaction with regards to specific products in orderto determine an effectiveness of the customer-employee interactionidentified at 134. Through such determination, a facility may be able toidentify those departments or products in which the customer-employeeinteractions produce the largest increase in conversions or customerpurchases. This information may further help to inform and drive theemployee deployment decisions and suggestions, exemplarily fordepartment optimization at 132, such that employee resources aredeployed to those departments, locations, and products in which thefacility may see the largest return on improved customer service.

FIG. 4 is a system diagram of an exemplary embodiment of a computingsystem 200 for workforce optimization. In exemplary embodiments, thecomputing system 200 may be used to implement embodiments of theworkforce management server 16 and/or workforce allocator 18 asexemplarily described above with respect to FIG. 1. The computing system200 is generally a computing system that includes a processing system206, storage system 204, software 202, communication interface 208 and auser interface 210. The processing system 206 loads and executessoftware 202 from the storage system 204, including a software module230. When executed by the computing system 200, software module 230directs the processing system 206 to operate as described in herein infurther detail in accordance with the method 100 as described above withrespect to FIG. 2.

Although the computing system 200 as depicted in FIG. 4 includes onesoftware module in the present example, it should be understood that oneor more modules could provide the same operation. Similarly, whiledescription as provided herein refers to a computing system 200 and aprocessing system 206, it is to be recognized that implementations ofsuch systems can be performed using one or more processors, which may becommunicatively connected, and such implementations are considered to bewithin the scope of the description.

The processing system 206 can include a microprocessor and othercircuitry that retrieves and executes software 202 from storage system204. Processing system 206 can be implemented within a single processingdevice but can also be distributed across multiple processing devices orsub-systems that cooperate in executing program instructions. Examplesof processing system 206 include general purpose central processingunits, application specific processors, and logic devices, as well asany other type of processing devices, combinations of processingdevices, or variations thereof

The storage system 204 can include any storage media readable byprocessing system 206, and capable of storing software 202. The storagesystem 204 can include volatile and non-volatile, removable andnon-removable media implemented in any method or technology for storageof information, such as computer readable instructions, data structures,program modules, or other data. Storage system 204 can be implemented asa single storage device but may also be implemented across multiplestorage devices or sub-systems. Storage system 204 can further includeadditional elements, such a controller capable of communicating with theprocessing system 206.

Examples of storage media include random access memory, read onlymemory, magnetic discs, optical discs, flash memory, virtual andnon-virtual memory, magnetic sets, magnetic tape, magnetic disc storageor other magnetic storage devices, or any other medium which can be usedto store the desired information and that may be accessed by aninstruction execution system, as well as any combination or variationthereof, or any other type of storage medium. In some implementations,the storage media can be a non-transitory storage media.

User interface 210 can include a mouse, a keyboard, a voice inputdevice, a touch input device for receiving a gesture from a user, amotion input device for detecting non-touch gestures and other motionsby a user, and other comparable input devices and associated processingelements capable of receiving user input from a user. In embodiments,the user interface 210 operates to present and/or to receive informationto/from a user of the computing system. Output devices such as a videodisplay or graphical display can display an interface further associatedwith embodiments of the system and method as disclosed herein. Speakers,printers, haptic devices and other types of output devices may also beincluded in the user interface 210.

As described in further detail herein, the computing system 200 receivesand transmits data through the communication interface 208. Inembodiments, the communication interface 208 operates to send and/orreceive data to/from other devices to which the computing system 200 iscommunicatively connected. In the computing system 200, video data 250is received. The video data 250 may exemplarily come directly from aplurality of video cameras as depicted in FIG. 1, while in otherembodiments the video data 250 is exemplarily stored at a computerreadable medium which may be remotely located form the computing system.In a still further embodiment, the video data 250 is received by thecomputing system 200 from an intermediate computer (not depicted) thatperforms initial video processing on the video data, exemplarily toidentify people in the video data or provide an initial count of peoplein the video data. As described above, the computing system 200 alsoreceives employee schedules 220, employee data 230, and department data240 which is all exemplarily stored on one or more computer readablemedia. The computing system 200 executes the application module 230exemplarily to carry out an embodiment of the method 100 as describedherein.

The computing system 200 processes the video data 250 in order toidentify, count, and/or track people in the video data 250. Thecomputing system further receives employee schedules 220, employee data230, and department data 240 and uses this information along with theidentified people in the video data to produce employee deployments 260which are sent to one or more remote computing devices, exemplarily oneassociated with a manager. The computing system 200 also may outputemployee deployment analytics 270 on a graphical display or other outputdevice. Such employee deployment analytics 270 may be used by a manageror other personnel to evaluate store operation or to adjust or modifydepartment data 240.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to make and use the invention. The patentable scope of the inventionis defined by the claims, and may include other examples that occur tothose skilled in the art. Such other examples are intended to be withinthe scope of the claims if they have structural elements that do notdiffer from the literal language of the claims, or if they includeequivalent structural elements with insubstantial differences from theliteral languages of the claims.

1. A method of workforce optimization, the method comprising: acquiringvideo data obtained from a plurality of video cameras in a facilitycomprising a plurality of departments; analyzing the video data todetermine a customer-to-employee ratio for each department, wherein theanalysis comprises: identifying a customer load for each of theplurality of departments, wherein the customer load for a department isthe number of people identified in the video of the department, andidentifying a location of each of a plurality of employees in thefacility, wherein the identification of a location of a particularemployee comprises analyzing the acquired video data to identify theparticular employee based on an identifiable feature worn by allemployees including the particular employee; determining acustomer-to-employee ratio for each department from the identifiedcustomer load and the identified location of each of the plurality ofemployees; generating an employee deployment based on thecustomer-to-employee ratio determined for each department; and providinga message corresponding to the employee deployment to a mobile computingdevice.
 2. The method according to claim 1, wherein the mobile computingdevice is associated with a manager.
 3. The method according to claim 2,wherein the message corresponding to the employee deployment comprises aplurality of suggested employee deployments, wherein each suggestedemployee deployment identifies one or more employees for deployment. 4.The method according to claim 3, further comprising: receiving aselection of at least one of the plurality of the suggested employeedeployments from the mobile computing device associated with themanager; and transmitting a deployment notification to one or moremobile computing devices associated with the one or more employeesidentified for deployment.
 5. The method according to claim 1, whereinthe mobile computing device is associated with an employee.
 6. Themethod according to claim 5, wherein the message corresponding to theemployee deployment comprises a plurality of response selections.
 7. Themethod according to claim 6, further comprising: receiving a selectionof one of the plurality of the response selections from the mobilecomputing device associated with the employee; and transmitting theselected response selection to a mobile computing device associated witha manager.
 8. The method according to claim 1, wherein the generating anemployee deployment based on the customer-to-employee ratio determinedfor each department, comprises: comparing, for each department, thedetermined customer-to-employee ratio with a target customer-to-employeeratio for the department; concluding that a particular department isoverstaffed or understaffed based on the comparison; and generating anemployee deployment for the particular department.
 9. The methodaccording to claim 8, wherein the generating an employee deployment forthe particular department comprises: identifying one or more employeesfor deployment based on their location relative to the particulardepartment.
 10. The method according to claim 8, further comprising:determining a priority of the employee deployment based on thecomparison.
 11. A system for workforce optimization, the systemcomprising: a plurality of video cameras positioned throughout afacility to capture video of a plurality of departments in the facility;one or more mobile computing devices; and a workforce management serverin communication with the plurality of video cameras and the one or moremobile computing devices, wherein the workforce management servercomprises a processor that executes software stored in memory to:acquire video data obtained from the plurality of video cameras; analyzethe video data to determine a customer-to-employee ratio for eachdepartment, wherein the analysis causes the processor to: identify acustomer load for each of the plurality of departments, wherein thecustomer load for a department is the number of people identified in thevideo of the department, and identify a location of each of a pluralityof employees in the facility, wherein the identification of a locationof a particular employee comprises analyzing the acquired video data toidentify the particular employee based on an identifiable feature wornby all employees including the particular employee; determine acustomer-to-employee ratio for each department from the identifiedcustomer load and the identified location of each of the plurality ofemployees; generate an employee deployment based on thecustomer-to-employee ratio determined for each department; and providinga message corresponding to the employee deployment to the one or moremobile computing devices.
 12. The system according to claim 11, whereinthe one or more mobile computing devices includes a mobile computingdevice associated with a manager.
 13. The system according to claim 12,wherein the message corresponding to the employee deployment provided tothe mobile computing device associated with the manager comprises aplurality of suggested employee deployments, wherein each suggestedemployee deployment identifies one or more employees for deployment. 14.The system according to claim 13, where the processor executes softwarestored in memory to: receive a selection of at least one of theplurality of the suggested employee deployments from the mobilecomputing device associated with the manager; and transmit a deploymentnotification to one or more mobile computing devices associated with theone or more employees identified for deployment.
 15. The systemaccording to claim 11, wherein the one or more mobile computing devicesincludes a mobile computing device associated with an employee.
 16. Thesystem according to claim 15, wherein the message corresponding to theemployee deployment provided to the mobile computing device associatedwith the employee comprises a plurality of response selections.
 17. Thesystem according to claim 16, further comprising: receiving a selectionof one of the plurality of the response selections from the mobilecomputing device associated with the employee; and transmitting theselected response selection to a mobile computing device associated witha manager.
 18. The system according to claim 11, wherein the generatingan employee deployment based on the customer-to-employee ratiodetermined for each department, comprises: comparing, for eachdepartment, the determined customer-to-employee ratio with a targetcustomer-to-employee ratio for the department; concluding that aparticular department is overstaffed or understaffed based on thecomparison; and generating an employee deployment for the particulardepartment.
 19. The system according to claim 18, wherein the generatingan employee deployment for the particular department comprises:identifying one or more employees for deployment based on their locationrelative to the particular department.
 20. A non-transitory computerreadable medium programmed with computer readable code that uponexecution by a computer processor causes the processor to: acquire videodata obtained from a plurality of video cameras in a facility comprisinga plurality of departments; analyze the video data to determine acustomer-to-employee ratio for each department, wherein the analysiscomprises: identifying a customer load for each of the plurality ofdepartments, wherein the customer load for a department is the number ofpeople identified in the video of the department, and identifying alocation of each of a plurality of employees in the facility, whereinthe identification of a location of a particular employee comprisesanalyzing the acquired video data to identify the particular employeebased on an identifiable feature worn by all employees including theparticular employee; determine a customer-to-employee ratio for eachdepartment from the identified customer load and the identified locationof each of the plurality of employees; generate an employee deploymentbased on the customer-to-employee ratio determined for each department;and provide a message corresponding to the employee deployment to amobile computing device.